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dc.contributor.authorTan, Yan Hwaen_US
dc.description.abstractIn recent years, the fast-moving consumer goods (FMCG) industry has shown significant interest in robot warehouse automation technology due to the increasing demand of e-commerce, fast and reliable delivery. However, it is not a simple task to pack a large variety of products according to mass customized orders. Therefore, a fully autonomous warehouse pick-and-place system is able to complete the job with ease by employing a robust vision system that reliably locates and recognizes objects from cluttered environment, different objects and self-occlusions. The aim of this project is to develop an automated solution to allow the robot to pick up the indicated object accurately from a clustered bin in bin-picking. The robot system setup consists of a UR5 robotic arm attached with a gripper and a vision camera. In the proposed approach, we segmented and labelled multiple perspective of a view using a convolutional neural network. A large amount of training data is required to train a deep neural network for segmentation. Therefore, the proposed solution used a self-supervised method to train a large dataset and at a faster speed. The Mask-R-CNN approach was also implemented to identify each item and their individual masks to achieve a higher accuracy for object detection and image segmentation.en_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Mechanical engineeringen_US
dc.titleDeep learning for object detection and image segmentationen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorDino Accotoen_US
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.description.degreeBachelor of Engineering (Mechanical Engineering)en_US
dc.contributor.organizationA*STAR Advanced Remanufacturing and Technology Center (ARTC)en_US
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Appears in Collections:MAE Student Reports (FYP/IA/PA/PI)
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